Senior Solutions Architect, Generative AI Research

Nvidia

Remote

Quick summary

Work type: Remote
Location: Remote
Salary: $184,000–$287,500 / yr
Posted: 4 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $211k

This role $236k

$163k most similar roles pay here $301k

This role pays more than 69% of similar roles. Most pay $176,000–$246,150 — the shaded band above. At the midpoint, this role pays about $236k versus about $211k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 950 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 939 roles with salary data.

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At a glance

TL;DR · Senior Solutions Architect, Generative AI Research

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Join NVIDIA’s Higher Education and Research Team as a Senior Solutions Architect to support university researchers advancing foundation models, multimodal AI, reasoning systems, and AI agents. You will partner with academic developers on GPU-accelerated training, inference studies, agent evaluation, and scalable systems, while building research prototypes using the full NVIDIA stack. Your responsibilities include analyzing performance metrics across various computing environments and translating lab feedback into technical guidance for NVIDIA teams. Ideal candidates have a BS, MS, or PhD in Computer Science or related fields with extensive experience in AI systems, distributed training, and generative AI workflows. Proficiency in PyTorch, JAX, Python, Linux, and distributed AI is essential, along with expertise in LLMs, VLMs, multimodal models, reasoning, and evaluation methods. Experience guiding research teams on benchmarks and reproducibility is crucial, as is a commitment to advancing AI scholarship through publications and open-source contributions.

Skills

NVIDIA PyTorch JAX Python Linux Distributed AI Data Loading Checkpointing Memory Optimization Batching Scheduling Latency Throughput NVIDIA NeMo TensorRT-LLM Trident RAPIDS LLMs VLMs Multimodal Models Reasoning Systems Fine-Tuning Post-Training Agentic AI Evaluation LangGraph LlamaIndex LangChain CrewAI AutoGen Semantic Kernel Google ADK OpenAI Agents SDK DSPy MCP A2A

What you'll do

Partner with universities to shape high-impact work on AI systems and models.
Advise labs on GPU-accelerated training, inference studies, and agent evaluation methods.
Help build research prototypes using the NVIDIA full stack for academic developers.
Analyze performance metrics across different computing environments for scalability.
Translate lab feedback into technical examples and guidance for NVIDIA teams.

What we're looking for

BS, MS or PhD in a relevant technical field or equivalent experience.
8+ years of hands-on AI systems and accelerated computing experience.
Deep expertise in LLMs, VLMs, multimodal models, reasoning, fine-tuning.
Strong fluency in PyTorch/JAX, Python, Linux, distributed AI systems.
Experience guiding academic teams on benchmarks, reproducibility, safety.
Clear communication skills and ability to translate complex questions into practical steps.
Familiarity with NVIDIA NeMo tools and other AI agent methods.

Senior Solutions Architect, AI Infrastructure

Nvidia

Austin, TX +3 32 days ago $184,000–$287,500

NVIDIA_Ethernet InfiniBand GPUs CPUs PCIe DPUs NICs HCAs switches rack_scale_design system_hardware_architecture kernel_drivers PCIe_devices AI_data_centers POCs_for_AI_solutions

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